AUGMENTING EMPIRICAL STAND PROJECTION EQUATIONS WITH EDAPHIC AND CLIMATIC VARIABLES

Builders of management growth and yield models have shown ingenuity in supplying projection equations with additional variables (for example , site index, time and amount of thinning) to enhance the quality of p redictions. Other variables, edaphic or mechanistic in origin, have no t been utilised because of difficulties in obtaining precise areal est imates at an affordable cost. Environmental (for example, rainfall, so lar radiation) data have become available through response surface spl ining algorithms using data from weather station networks; long-term c limate averages are available at any chosen location, This paper descr ibes the building of mean-top-height and basal area ha(-1) projection equations, utilising climate variables in conjunction with traditional plot measures. The data were secured from the: Nelson region of New Z ealand, where stands of Pinus radiata are established on four contrast ing soil groupings. No improvement in precision was found for the pred iction of mean-top-height, by including temperature, solar radiation, or rainfall data, nor by recognising the diverse soils. Conversely, an improvement of 10% was obtained in modelling basal area ha(-1). Radia tion and rainfall (but not temperature) significantly improved precisi on and accuracy, varying in functional form by soil type. The individu al effects of soil-type and climate are heavily confounded. It is argu ed that forest process and empirical-based modelling has been independ ently researched for too long. There is evidence enough to suggest tha t hybrid modelling, encompassing both approaches, could improve the pr edictive ability of current growth prediction systems. (C) 1997 Elsevi er Science B.V.